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Anglický jazyk
Neural Network Algorithm for LDA/GSVD
Autor: Rolysent Paredes
The capability of the classical Linear Discriminant Analysis based on Generalized Singular Value Decomposition (LDA/GSVD) deteriorates when dealing with unlabeled datasets because LDA requires predefined inputs and targets. In addition, the LDA/GSVD algorithm... Viac o knihe
Na objednávku
36.99 €
bežná cena: 41.10 €
O knihe
The capability of the classical Linear Discriminant Analysis based on Generalized Singular Value Decomposition (LDA/GSVD) deteriorates when dealing with unlabeled datasets because LDA requires predefined inputs and targets. In addition, the LDA/GSVD algorithm suffers from high computation cost due to its complex mathematical calculations and iterations. To address these problems, this study introduces Self-Organizing Map (SOM) as a new method in labeling datasets, and the development of an Artificial Neural Network-based algorithm to overcome the computational cost of LDA/GSVD. The results show that using SOM and ANN are effective in solving the problems of the traditional LDA/GSVD algorithm.
- Vydavateľstvo: LAP LAMBERT Academic Publishing
- Rok vydania: 2019
- Formát: Paperback
- Rozmer: 220 x 150 mm
- Jazyk: Anglický jazyk
- ISBN: 9783330347809
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